PAPER: The Power and Potential of Psychological Assessment Around the World -- Rethinking Traditional Paradigms
Bibliographic record
Abstract
Assessment is a broad, overarching term and multifaceted process. Arguably, it is the sine qua non of psychological research and practice. Without it, psychology’s worldwide contributions to education, business, mental health, public policy, and other areas would be diminished. Although many people believe assessment, particularly testing, is a static, reductionist process, this is often not the case. Rather, assessment can be a dynamic, excitingly rich, and remarkably transformative process. For this 10-15 minute presentation, objectives include: (1) increased familiarity with diverse approaches to assessment and testing, (2) increased awareness of existing literature and empirical bases of, for example, collaborative/therapeutic assessment approaches, and (3) re-consideration of “best practices” in testing, particularly in international contexts. The presentation is based, in part, on a soon-to-be-published book chapter co-authored by the presenter(s), many of whom are past, or current, presidents of international organizations. The book is called, “Going Global: How Psychology and Psychologists Can Meet a World of Need (APA Books).” Because we live in a complex, highly diverse world, it’s important, the authors believe, to collect both quantitative and qualitative data; collaborate with constituents as much as possible; ask good, meaningful, culturally appropriate questions; and attend closely to (and reflect on) assessment-related processes, just as much as outcomes. It is also important to think big, start small, and go slow. These practices will be presented. Sufficient time will be allowed for questions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.022 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.025 |
| Scholarly communication | 0.024 | 0.028 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.005 | 0.011 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".